Skip to main content
Glama
yesid-lopez

MLflow MCP Server

by yesid-lopez
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Related Servers

Alternatives to MLflow MCP Server

No user-submitted related servers found.

    Related Servers

    • tracker-mcp

      Lesvitadsouza
      F
      license
      Not graded
      quality
      C
      maintenance
      Enables querying machine-learning experiments in natural language. Supports listing, inspecting, ranking, and comparing experiment runs.
      -

    • MLOps MCP Server

      elliottdevo8
      A
      license
      Not graded
      quality
      D
      maintenance
      Enables Claude to interact with ML experiment tracking, model registries, and deployment pipelines across popular MLOps platforms like MLflow.
      MIT

    • mlops-mcp-server

      anant-patankar
      A
      license
      D
      quality
      D
      maintenance
      Enables AI assistants to perform MLOps workflows such as experiment tracking, model registry, dataset management, pipeline orchestration, and data lineage by wrapping DVC, MLflow, and Git.
      100
      1
      MIT

    • mlctl

      isouravsengupta
      F
      license
      Not graded
      quality
      C
      maintenance
      Enables natural language management of the full ML lifecycle including experiments, model registration, deployment, and pipeline orchestration through a conversational agent.
      -

    • mcp-wandb

      tsilva
      A
      license
      B
      quality
      C
      maintenance
      Enables LLM agents to query Weights & Biases experiments, including listing projects, runs, metrics, plotting metrics, and retrieving run details.
      5
      1
      MIT
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    C2.5/5.0

    Scored across 7 tools

    Disambiguation4/5

    Most tools have distinct purposes targeting different MLflow entities (experiments, runs, models), but 'get_experiment' and 'get_experiment_by_name' could cause confusion as they serve the same ultimate purpose through different lookup methods. The other tools clearly target different resources.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern with 'get_' or 'search_' prefixes, using snake_case throughout. The naming is predictable and follows a clear convention across all seven tools.

    Tool Count5/5

    Seven tools is well-scoped for an MLflow server, covering key entities like experiments, runs, and models. The count feels appropriate—neither too sparse nor bloated—for the apparent domain scope.

    Completeness3/5

    The toolset provides good read/search coverage for experiments, runs, and models, but lacks any write operations (create, update, delete) or lifecycle management tools. This is a notable gap that will limit agent workflows to read-only scenarios.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources